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authorzsloan2015-03-27 20:28:51 +0000
committerzsloan2015-03-27 20:28:51 +0000
commitd0911a04958a04042da02a334ccc528dae79cc17 (patch)
tree3c48e2e937c1dbeaf00a5697c87ed251afa5c8f1 /web/tutorial/ppt/WebQTLDemo_files/slide0053.htm
parenta840ad18e1fe3db98a359a159e9b9b72367a2839 (diff)
downloadgenenetwork2-d0911a04958a04042da02a334ccc528dae79cc17.tar.gz
Removed everything from 'web' directory except genofiles and renamed the directory to 'genotype_files'
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- <layer> <div id=NotesObj style='display:none'> <table style='color:white' border=0 width="100%"> <tr> <td width=5 nowrap></td> <td width="100%"></td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><font face="Times New Roman" size=4><b>Candidate Genes: </b><span style="mso-spacerun: yes">&nbsp;</span>The best we can do at this point is to make an educated guess about the candidacy status of all genes in the QTL support interval. For sake of argument, lets say that we are confident that the polymorphism is located between 130 and 150 Mb (20 Mb, equivalent to roughly 10 cM). There will typically be 12 to 15 genes per Mb, so we now would like to evaluate 240 to 300 positional candidates. We would like to highlight the biologically relevant subset of candidates. We could look through gene ontologies and expression levels to help us winnow the list. An alternate way avaiable using WebQTL is to generate a list of those genes in this 20 Mb interval that have transcripts that co-vary in expression with App expression.</font><br> </td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><br> </td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><font face="Times New Roman" size=4>To do this, go back to the Trait Data and Editing window. Sort the correlations by position. Select Return = 500. Then scroll down the list to see positional candidates that share expression with App.</font><br> </td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><br> </td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><font face="Times New Roman" size=4>There are several candidates that have high correlation with App even in this short 20 Mb interval. We can rank them by correlation, but they are all close.<span style="mso-spacerun: yes">&nbsp; </span>There is one other imporant approach to ranking these candidates. Are they likely to contain polymorphisms? We can assess the likelihood that they contain polymorphisms by mapping each transcript to see if any have strong cis QTLs. The logic of this search is that a transcript that has a strong cis-QTL is likely to contain functional polymorphisms that effect its own expression. This make is more like that the transcript is a ÒcausativeÓ factor since it is likely to be polymorphic.</font><br> </td> </tr> <tr> <td colspan=1></td> <td align=left colspan=1><br> </td> </tr> </table> </div> </layer> <script language=JavaScript><!--
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